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1 Week 7 Amare Michael Desta Decision Support & Executive Information Systems:

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2 1 Week 7 Amare Michael Desta Decision Support & Executive Information Systems:

3 2 Data, Information and Knowledge is needed to … To manage internal operations React to the external environment, to potential threats and opportunities Manage/Minimise risk Generate knowledge, ideas and, through this, - New way(s) of doing things and - New Products & Services may be achieved

4 3 The Naïve View Data is what we find in databases - But how does the database know what data to hold? Information is what we find in IS and it allow us to ask questions of the data. - But how does the information system know what questions we will want to ask?

5 4 Data & Data Values Data – that which is given In problem solving (decision making) What is known or assumed to be true Typically a member or members of one or more collection or sets of objects E.g. in Mathematics – Given a line and a point not on that line … Data = any one individual member of the set of lines and any one individual member of the set of points that satisfies the condition.

6 5 Data & Data Values In engineering we move from the individual to the particular From the mathematical concept of a line to the practical realisation of this particular line from here to there.

7 6 Relational Data Tables

8 7 Data, Measurement and Observation A chicken and egg situation There can be no observation without knowledge We have to decide what something is – to categorise it before we can measure it and record the data values.

9 8 Reason Reason derives from the same root meaning as ratio and also connected with relation Connected to the idea of the balance To weigh the evidence To put things in proportion

10 9 Decision Theory 1 Decisions consist of: A set of possible courses of action A set of outcomes form each action A state of the world

11 10 Decision Theory 2 Decision making contexts can be classified in terms of the Information and knowledge available - Certainty - Risk - Uncertainty

12 11 Rationality When modelling peoples behaviour economists and management scientists usually assume that people are rational which means that: - They always choose their best option the one that maximises their payoff - Which means they have the knowledge and ability to determine what their best option is!

13 12 Bounded Rationality Problems with assuming rational actors - It is very easy to provide counter examples from experience - Most people are not in possession of enough information (data) to determine what their best option is - Most people do not have the necessary knowledge to determine their best option even given the necessary information

14 13 Bounded Rationality Herbert Simon introduced the term bounded rationality as a more realistic view of decision making: - BR is NOT irrationality - Actors make the best decision (act rationally) given - limited information - Limited knowledge - Limited resources

15 14 Learning & Knowing process Requires an understanding of: Know who Know where Know when Know what Know about Know how

16 15 Learning & Knowing 2 Categorisation & Knowledge - Similarity – common properties - Difference – distinct properties - Contiguity – at the same place and or at the same time

17 16 Knowledge Management in Organisations Knowledge Management, (KM), is: Systemically and actively managing and leveraging stores of knowledge in organisation

18 17 Knowledge Management Systems, (KMS) KMS – are sophisticated decision support systems KMS – Support Decision Support Systems KMS – Are systems for managing the knowledge processes of an organisation

19 18 Information and Knowledge Work Systems DATA WORKERS: People who process & Disseminate organizations paperwork INFORMATION WORK: Work consists primarily of creating, processing information KNOWLEDGE WORKERS: People who design products or services or create new knowledge for organization

20 19 Knowledge Processes 1 A Mechanistic View People as Computers Creation Acquisition Transmission Storage Retrieval


22 21 Organisational Knowledge

23 22 Bohns Stages of Knowledge

24 23 Data, Information & Knowledge in Use

25 24 Relationship of Data, Information & Knowledge The World The Agents Knowledge Base Data Filters Data Information Prior Expectation Knowledge: a disposition to act

26 25 Processing Hierarchy Centrality of data (Wilson 1996) Does data always lead to information? Does information always lead to knowledge? And where does good judgement come from? Action Decision Knowledge Information Data

27 26 Data Systems & Knowledge Intelligence in Data Processing Systems Processing Report Manipulation Data Entry Data Collection USERS Knowledge is a pervasive characteristic of information systems

28 27 Data & Information: System Perspective DataInformation Sender Receiver Encoding Decoding Computer System

29 28 Information Systems Information systems process data and turn it into information that can be used for management decision-making Knowledge is used to design, encode and operate IS Knowledge is needed to decode the information that comes out of IS IS professionals need to understand the human (perceptual) processes involved in the encoding and decoding process

30 Data, Information & Knowledge: Linear Models KnowledgeActionsDataInformationResults Benefits-Driven Approach Usual Approach

31 30 Data, Information & Knowledge Cyclic Model Accumulate Knowledge (Experience) Format, Filter Summarise Interpret, Decide, Act Knowledge Information Results Data

32 31 Information & Knowledge: Sharing & Transmission Information Capture, creation and dissemination Releasing the Value by use and re-use Knowledge – transmission(s) Explicit to Explicit Tacit to explicit Explicit to tacit. Tacit to Tacit. Nonanka (1991)

33 32 Information & Quality – the main issues Accurate Appropriate detail and accuracy for the user Meaningful to user Up to date - information is very time sensitive. Out of date information is misleading if not useless. Available at point of need/use. related to decision-maker's context Complete and comprehensive Providers the receiver with all they need to know

34 33 Information & Quality Format in a form that aids assimilation. Cost effective costs of production and delivery lower than the benefits derived. e.g. a decision taken on the basis of the information provided could result in reduced costs, increased sales/revenue, better utilisation of resources Must be secure BUT.... the potential value of information depends on its quality.

35 34 Historical Trends Massive structural shifts in Western economies Knowledge Data Information Represented in Technology Shift in Importance

36 35 The Changing Economic Eras

37 36 Evolution of the Nature of the Economy Wilson, Managing Knowledge, 1994 Agricultural Information Industrial Service

38 37 The Shift to Information & K Work Shift away from agriculture and manufacturing to services Outsourcing of manufacturing to the Developing World. Trend towards knowledge-based manufacturing Increased growth in knowledge-based products and knowledge- enhanced goods – mobile phones, CDs, digital photos, electronic journals Growth in information and Knowledge-based services, particularly in advanced economies Massive growth in information based occupations & knowledge work. In the US, over 85% population works in services, with 65% in high skilled areas. Fastest growth areas: education, communications and information, computing, electronics and aerospace industries

39 38 Key Drivers of Change Political Changes Collapse of Communism, formation of major economic and political alliances Business Changes Growth of free trade, deregulation, emergence of new producers, globalisation Technological Changes Biotechnology, telecommunications and computing Social Changes Stakeholder Society, spread of egalitarian ideal

40 39 The New Economy: Key Points Key driver is INNOVATION rather than production efficiency (quality rather than quantity) Knowledge is the main source of innovation Economic wealth depends on knowledge creation, production and distribution Organisations are increasingly information and knowledge-based Workforce is more skilled and knowledgeable State and employers invest heavily in research and development in science and technology Greater investment in education and training

41 40 The Emergence of IM & KM IM & KM are new fields of study Multi-disciplinary Focus on information and managing expertise not on technology – IT underpins information and knowledge management New type of professional may be required – one who understands information, Knowledge, IT and business

42 41 Information Use: Management Issues What information do we need? What information do we have? Where is the information held and in what form? How much does it cost to acquire and process information? How can we tell if it adds any value?

43 42 Knowledge Use: Management Issues What information is needed to create knowledge? What explicit knowledge do we have? Where can we find it? What implicit knowledge do our employees have? How can we capture and use it? How much value does knowledge add? How can we cultivate knowledge within the organisation?

44 43 Why Knowledge Management? Organisations have lots of useful knowledge lying around that could be used to their advantage But identifying it, finding it and leveraging it can be problematical A knowledge intensive culture promotes knowledge creation and knowledge sharing

45 44 Taxonomy of Knowledge Tacit – rooted in actions, experience & context Explicit – articulated, generalized Social - know who – collective Declarative – know about Procedural – know how Causal – know why Conditional – know when Relational – know with Pragmatic – best practice

46 45 Basic Knowledge Processes Knowledge creation Knowledge storage & retrieval Knowledge transfer Knowledge application

47 46 Knowledge Creation Development of new tacit/explicit knowledge – individual & social Modes: Socialization, externalisation, internalisation, combination IS Data mining & data warehousing CSCW, intranets Brainstorming at a distance

48 47 Knowledge Storage & Retrieval Organisational memory Documents (hard & soft), databases, expert systems, plus tacit knowledge Supports status quo May not always be easy to interpret

49 48 Knowledge Transfer – can be achieved Between individuals, groups, explicit sources, organisations Depends on: - perceived value of source units knowledge, - willingness to share, - willingness to listen, - richness of transmission channel (implications for IS) - absorptive capacity of recipient.

50 49 Issues (i.e. Problems) in Practice Using KM for strategic advantage Obtaining top management support Motivating staff to contribute Identifying relevant knowledge Evaluation Verification Design & development Sustaining progress Security

51 50 Tacit Knowledge We know more than we can tell Hard to formalise & communicate Driving a car Explicit knowledge may imply tacit knowledge Polimorphic knowledge, relating to social behaviour, can only be learned through experience and socialisation

52 51 The Role of Experts Usually provides a certain status Unlikely to give away years of experience for nothing Experts often linked in a community of practice Experts often disagree Experts can be wrong but may be more likely to spot things going wrong and have sufficient judgement to change course

53 52 KM – A Dehumanising Technology? The next fad to forget people? (e.g. BPR) The idea behind KM is to stockpile workers knowledge and make it accessible via a searchable application KM emphasis is on IT, not HR Knowledge treated as a codified commodity Danger of increased rigidity Impact on remaining people – alienation?

54 53 Characteristics of Data, Information & Knowledge Data Explicit Use Accept No learning Direction Efficiency Information Interpreted Construct Confirm Single loop Communication Effectiveness Knowledge Tacit/embedded Reconstruct Disconfirm Double loop Sense-making Innovation

55 54 Information Management Infrastructure Identifying & meeting information requirements Assessing the cost of obtaining and processing information and the systems and staff needed to do it Appointing people with responsibility for managing information and IT resources Creating divisions, departments or sections responsible for managing information

56 55 Putting the Right People in Charge Chief Information Officer Chief Knowledge Officer Chief Technology Officer

57 56 Comparison of Roles CIOCTOCKO Manage internal information, IT & administrative resources Monitor, evaluate & select new technologies Transforming intellectual capital into business value Develop IT strategy & link it to business Provide technical vision to complement the business vision Identify knowledge requirements & strategies for increasing knowledge Ensure operational efficiency of systems Determine what technologies will generate best ROI Design & implement knowledge infrastructure Educate business in the use of IT Translate ideas into a form that laypeople understand Create collaborative work environment

58 57 The Chief Information Officer Role emerged in the mid 1980s Earl (1996) argues that it was a result of: Convergence of computing & telecommunications & consequent need to manage complex IT infrastructure Increased size of IT departments and budgets Realization that information & IT were strategic resources

59 58 The Chief Information Officer: Role In reality, often about managing IT CIO role has a very high turnover rate High project failure rate/soaring costs Inability of IT to support business goals and innovation Many organisations are devolving responsibility for IT to the business units and eliminating the role

60 59 The Chief Information Officer Earl (1996) found that the following attributes were critical: - Very high level of technical competence - Excellent leadership skills – ability to create a shared vision, good at relationship-building, ability to deliver - Good at politicking - Extroverted

61 60 The Chief Information Officer: CIO Genus Gartner Group Research (2000) - Strategist - Enterprise-wide responsibility for IM & IT management - Technologist - Enterprise-wide responsibility for ensuring technology-based services across the enterprise deliver - Technology opportunist - Executive-level responsibility for spotting the opportunity to use new technology - Executive - Head of business unit responsible for managing IT-related services

62 61 Conclusion Major changes in the sources of wealth creation have transformed the value of information & made knowledge a key organisational resource Organisations need to manage their information & knowledge resources effectively This requires an understanding of what information is and how it can best be captured, stored, disseminated and used to generate knowledge The task for managers is to create an infrastructure to exploit information and knowledge resources The appointment of senior staff to manage IT and Knowledge is a recognition of the importance of information but the high turnover rate suggests that information is frequently not well managed

63 62 Conclusion (Contd….) Like its forerunners (DM & IM) KM is encountering problems that mere technology cannot solve The blind application of KM principles is unlikely to be very successful but some useful tools may be developed along the way, together with vast amounts of (un)usable data

64 63 Conclusion (Contd….) All decision support systems involve data, information and knowledge When designing decision support system it is important to identify what data, information and knowledge is relevant to the problem Having too much or the wrong data, Wrong information or Wrong knowledge can be even more problematic than having too little.

65 64 Sources Earl, M.J. (1996) Information Management: The Organizational Dimension, Oxford University Press. Harrison, R. and Kessels, J. (2004) Human Resource Development in a Knowledge Economy: An Organisational View, Palgrave MacMillan. Kaku, M. (1998) Visions, Oxford University Press Pralahad,, C.K. and Ramaswamy, V. (2002) The Co-creation Connection, Strategy & Business, Issue 27, 2 nd Quarter: 50-61.

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